Optimizing Handwritten Arabic Character Recognition: Feature Extraction, Concatenation, and PSO-Based Feature Selection.

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Abstract Recognizing and writing Arabic characters correctly is a difficult task, especially for non-native speakers. With the growing trend toward digital teaching and distance learning, there is a need for efficient and accurate automatic recognition of Arabic characters. The work investigates three distinct approaches to address this challenge. Firstly, features are extracted from handwritten Arabic character images using two pre-trained models: EfficientNet B2 and DenseNet 201. These extracted features are then employed for classification utilizing various classifiers. Secondly, the features obtained from both models are concatenated to enhance classification performance by leveraging the unique representations learned by each model. Lastly, the Particle Swarm Optimization (PSO) algorithm is integrated into the feature selection process, identifying the most relevant features that contribute to improved classification accuracy. The best test accuracies obtained were 85.94% in the first experiment, 88.73% in the second, and 90.20% in the third.

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